{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qAkXdLL2D25p"
   },
   "source": [
    "## Peft model evaluation using [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness)\n",
    "\n",
    "In this notebook, we are going to learn how to evaluate the finetuned lora model on the hellaswag task using lm-eval-harness toolkit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
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    "outputId": "c5482c79-ff56-4ffa-d20c-46c3d30d2cd5",
    "trusted": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 24.0 -> 24.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "# Install LM-Eval\n",
    "!pip install -q datasets evaluate lm_eval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uhUflrJXD25q"
   },
   "source": [
    "### First we will check the accuracy score on the hellaswag task for the base bert without finetuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-11-01T04:24:09.124656Z",
     "iopub.status.busy": "2024-11-01T04:24:09.123802Z",
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     "shell.execute_reply": "2024-11-01T04:31:18.165468Z",
     "shell.execute_reply.started": "2024-11-01T04:24:09.124605Z"
    },
    "id": "hwJIYD5KD25q",
    "outputId": "51e69f81-d048-46b2-9699-658d3ffc5f08",
    "trusted": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-01:20:45:03,210 INFO     [evaluator.py:164] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234 | Setting fewshot manual seed to 1234\n",
      "2024-11-01:20:45:03,211 INFO     [evaluator.py:188] Initializing hf model, with arguments: {'pretrained': 'bert-base-cased', 'dtype': 'bfloat16'}\n",
      "2024-11-01:20:45:03,213 INFO     [huggingface.py:129] Using device 'cuda:0'\n",
      "2024-11-01:20:45:03,450 INFO     [huggingface.py:481] Using model type 'default'\n",
      "2024-11-01:20:45:03,741 INFO     [huggingface.py:365] Model parallel was set to False, max memory was not set, and device map was set to {'': 'cuda:0'}\n",
      "If you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True.`\n",
      "2024-11-01:20:45:15,862 INFO     [task.py:415] Building contexts for hellaswag on rank 0...\n",
      "100%|██████████| 10042/10042 [00:02<00:00, 4477.77it/s]\n",
      "2024-11-01:20:45:18,875 INFO     [evaluator.py:489] Running loglikelihood requests\n",
      "Running loglikelihood requests: 100%|██████████| 40168/40168 [00:34<00:00, 1152.65it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'hellaswag': {'alias': 'hellaswag',\n",
       "  'acc,none': 0.24905397331208923,\n",
       "  'acc_stderr,none': 0.004315812968431576,\n",
       "  'acc_norm,none': 0.2439753037243577,\n",
       "  'acc_norm_stderr,none': 0.004286002710084076}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import lm_eval\n",
    "\n",
    "\n",
    "output = lm_eval.simple_evaluate(model = 'hf',\n",
    "                        model_args = {\n",
    "                            'pretrained' : 'bert-base-cased',\n",
    "                            'dtype' : 'bfloat16'},\n",
    "                        tasks = 'hellaswag',\n",
    "                        device = 'cuda:0',\n",
    "                        batch_size = 128,\n",
    "                        log_samples = False)\n",
    "output[\"results\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EZk-1JT7D25r"
   },
   "source": [
    "### Now lets try to finetune the bert on the imdb dataset (this is for demonstration and finetuning on imdb may not increase the scores on hellaswag task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-01T04:31:18.168035Z",
     "iopub.status.busy": "2024-11-01T04:31:18.167698Z",
     "iopub.status.idle": "2024-11-01T04:31:19.278584Z",
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    },
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   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import evaluate\n",
    "import numpy as np\n",
    "from datasets import load_dataset\n",
    "from transformers import AutoTokenizer, BertForSequenceClassification, Trainer, TrainingArguments\n",
    "\n",
    "from peft import LoraConfig, TaskType, get_peft_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-11-01T04:31:19.282259Z",
     "iopub.status.busy": "2024-11-01T04:31:19.281242Z",
     "iopub.status.idle": "2024-11-01T04:31:19.565464Z",
     "shell.execute_reply": "2024-11-01T04:31:19.564510Z",
     "shell.execute_reply.started": "2024-11-01T04:31:19.282213Z"
    },
    "id": "rHF7tzN9D25r",
    "outputId": "352ad9ab-2efc-41f8-c3d5-a7da05d9529b",
    "trusted": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 296,450 || all params: 108,608,260 || trainable%: 0.2730\n"
     ]
    }
   ],
   "source": [
    "# Configure LoRA for Sequence Classification\n",
    "lora_config = LoraConfig(\n",
    "    task_type=TaskType.SEQ_CLS,        # Set task type to sequence classification\n",
    "    target_modules=[\"query\", \"key\"]    # Specify target modules for LoRA tuning\n",
    ")\n",
    "\n",
    "# Initialize the BERT model for sequence classification\n",
    "model = BertForSequenceClassification.from_pretrained(\n",
    "    'bert-base-cased',\n",
    "    num_labels = 2\n",
    ")\n",
    "\n",
    "# Wrap the model with LoRA configuration\n",
    "model = get_peft_model(model, lora_config)\n",
    "\n",
    "model.print_trainable_parameters()\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
   ]
  },
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Map: 100%|██████████| 25000/25000 [00:06<00:00, 3799.73 examples/s]\n"
     ]
    }
   ],
   "source": [
    "# load the dataset\n",
    "dataset = load_dataset(\"imdb\")\n",
    "\n",
    "def tokenize_function(row):\n",
    "    return tokenizer(row[\"text\"], padding=\"max_length\", truncation = True)\n",
    "\n",
    "tokenized_datasets = dataset.map(tokenize_function, batched = True)\n",
    "\n",
    "train_dataset = tokenized_datasets[\"train\"]\n",
    "eval_dataset = tokenized_datasets[\"test\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
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   "source": [
    "# Define a function to compute evaluation metrics\n",
    "\n",
    "def compute_metrics(eval_pred):\n",
    "    logits, labels = eval_pred\n",
    "    predictions = np.argmax(logits, axis=-1)\n",
    "    metric = evaluate.load(\"accuracy\")\n",
    "    return metric.compute(predictions = predictions, references = labels)"
   ]
  },
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 13%|█▎        | 500/3910 [08:16<56:48,  1.00it/s]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'loss': 0.3296, 'grad_norm': 1.0583174228668213, 'learning_rate': 0.0017442455242966753, 'epoch': 0.64}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                  \n",
      " 20%|██        | 782/3910 [17:50<37:42,  1.38it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.2824118435382843, 'eval_accuracy': 0.8816, 'eval_runtime': 308.5134, 'eval_samples_per_second': 81.034, 'eval_steps_per_second': 1.267, 'epoch': 1.0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 26%|██▌       | 1000/3910 [21:14<45:16,  1.07it/s]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'loss': 0.2756, 'grad_norm': 1.9588807821273804, 'learning_rate': 0.0014884910485933505, 'epoch': 1.28}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 38%|███▊      | 1500/3910 [29:05<38:42,  1.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'loss': 0.2461, 'grad_norm': 0.7111016511917114, 'learning_rate': 0.0012327365728900255, 'epoch': 1.92}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                   \n",
      " 40%|████      | 1564/3910 [35:10<28:13,  1.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.2278510481119156, 'eval_accuracy': 0.9102, 'eval_runtime': 304.8594, 'eval_samples_per_second': 82.005, 'eval_steps_per_second': 1.283, 'epoch': 2.0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 51%|█████     | 2000/3910 [42:06<29:44,  1.07it/s]   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'loss': 0.2154, 'grad_norm': 1.8200898170471191, 'learning_rate': 0.000976982097186701, 'epoch': 2.56}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                   \n",
      " 60%|██████    | 2346/3910 [52:29<18:45,  1.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.20936396718025208, 'eval_accuracy': 0.91996, 'eval_runtime': 299.637, 'eval_samples_per_second': 83.434, 'eval_steps_per_second': 1.305, 'epoch': 3.0}\n"
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     "text": [
      " 64%|██████▍   | 2500/3910 [54:57<22:19,  1.05it/s]   "
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     "text": [
      "{'loss': 0.1988, 'grad_norm': 2.2769970893859863, 'learning_rate': 0.000721227621483376, 'epoch': 3.2}\n"
     ]
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 77%|███████▋  | 3000/3910 [1:02:52<14:23,  1.05it/s]"
     ]
    },
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     "text": [
      "{'loss': 0.1635, 'grad_norm': 1.54856538772583, 'learning_rate': 0.00046547314578005116, 'epoch': 3.84}\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                     \n",
      " 80%|████████  | 3128/3910 [1:09:56<09:30,  1.37it/s]"
     ]
    },
    {
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     "text": [
      "{'eval_loss': 0.21944810450077057, 'eval_accuracy': 0.92164, 'eval_runtime': 303.6132, 'eval_samples_per_second': 82.342, 'eval_steps_per_second': 1.288, 'epoch': 4.0}\n"
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     "text": [
      " 90%|████████▉ | 3500/3910 [1:15:48<06:22,  1.07it/s]   "
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      "{'loss': 0.1355, 'grad_norm': 1.006062626838684, 'learning_rate': 0.00020971867007672635, 'epoch': 4.48}\n"
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     "text": [
      "                                                     \n",
      "100%|██████████| 3910/3910 [1:27:17<00:00,  1.36it/s]"
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      "{'eval_loss': 0.2205527275800705, 'eval_accuracy': 0.92556, 'eval_runtime': 302.417, 'eval_samples_per_second': 82.667, 'eval_steps_per_second': 1.293, 'epoch': 5.0}\n"
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     "text": [
      "100%|██████████| 3910/3910 [1:27:17<00:00,  1.34s/it]"
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    {
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     "text": [
      "{'train_runtime': 5237.7573, 'train_samples_per_second': 23.865, 'train_steps_per_second': 0.747, 'train_loss': 0.213202116983321, 'epoch': 5.0}\n"
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      "\n"
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     "data": {
      "text/plain": [
       "TrainOutput(global_step=3910, training_loss=0.213202116983321, metrics={'train_runtime': 5237.7573, 'train_samples_per_second': 23.865, 'train_steps_per_second': 0.747, 'total_flos': 3.300271872e+16, 'train_loss': 0.213202116983321, 'epoch': 5.0})"
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     "execution_count": 13,
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   "source": [
    "# Configure training arguments\n",
    "training_args = TrainingArguments(\"bert-lora-imdb\",\n",
    "    eval_strategy=\"epoch\",\n",
    "    per_device_train_batch_size=32, # decrease this for OOM error\n",
    "    per_device_eval_batch_size=64,\n",
    "    save_strategy=\"epoch\",\n",
    "    learning_rate=2e-3,\n",
    "    num_train_epochs=5,\n",
    "    weight_decay=0.01,\n",
    "    load_best_model_at_end=True,\n",
    "    do_eval=True,\n",
    "    do_predict=True,\n",
    "    metric_for_best_model=\"accuracy\",\n",
    "    report_to=\"none\")\n",
    "\n",
    "# Initialize the Trainer for the model training loop\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=train_dataset,\n",
    "    eval_dataset=eval_dataset,\n",
    "    compute_metrics=compute_metrics,\n",
    ")\n",
    "\n",
    "#start training\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "34h3g_eED25s"
   },
   "source": [
    "### Now take the finetuned lora checkpoint and check the accuracy score on hellaswag task."
   ]
  },
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-01:23:37:57,640 INFO     [evaluator.py:164] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234 | Setting fewshot manual seed to 1234\n",
      "2024-11-01:23:37:57,641 INFO     [evaluator.py:188] Initializing hf model, with arguments: {'pretrained': 'bert-base-cased', 'peft': './bert-lora-imdb/checkpoint-3910', 'dtype': 'bfloat16'}\n",
      "2024-11-01:23:37:57,643 INFO     [huggingface.py:129] Using device 'cuda:0'\n",
      "2024-11-01:23:37:57,891 INFO     [huggingface.py:481] Using model type 'default'\n",
      "2024-11-01:23:37:58,161 INFO     [huggingface.py:365] Model parallel was set to False, max memory was not set, and device map was set to {'': 'cuda:0'}\n",
      "If you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True.`\n",
      "2024-11-01:23:38:10,295 INFO     [task.py:415] Building contexts for hellaswag on rank 0...\n",
      "100%|██████████| 10042/10042 [00:02<00:00, 4453.89it/s]\n",
      "2024-11-01:23:38:13,313 INFO     [evaluator.py:489] Running loglikelihood requests\n",
      "Running loglikelihood requests: 100%|██████████| 40168/40168 [00:44<00:00, 893.15it/s] \n",
      "2024-11-01:23:39:12,119 WARNING  [huggingface.py:1353] Failed to get model SHA for ./bert-lora-imdb/checkpoint-3910 at revision main. Error: Repo id must be in the form 'repo_name' or 'namespace/repo_name': './bert-lora-imdb/checkpoint-3910'. Use `repo_type` argument if needed.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'hellaswag': {'alias': 'hellaswag',\n",
       "  'acc,none': 0.2535351523600876,\n",
       "  'acc_stderr,none': 0.00434145484189232,\n",
       "  'acc_norm,none': 0.24875522804222266,\n",
       "  'acc_norm_stderr,none': 0.0043140816086246455}}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
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   ],
   "source": [
    "# use the path of your checkpoint here\n",
    "output = lm_eval.simple_evaluate(model = 'hf',\n",
    "                        model_args = {\n",
    "                          'pretrained' : 'bert-base-cased',\n",
    "                          'peft' : './bert-lora-imdb/checkpoint-3910',\n",
    "                          'dtype' : 'bfloat16'},\n",
    "                        tasks = 'hellaswag',\n",
    "                        device = 'cuda:0',\n",
    "                        batch_size = 128,\n",
    "                        log_samples = False)\n",
    "\n",
    "output[\"results\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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